CDFAM Computational Design Symposium — Tokyo 2026
Zinab Eisa & Esha Morakhiya · Harvard University
Aeroforms emerges from a shared curiosity of how the invisible sensory phenomena of scent can be modelled, mapped and ultimately understood as data, with a unique focus on scentscapes of Japan.
Scent is one of the most powerful triggers of memory and belonging, yet it remains largely absent from environmental modeling and urban analytics. In 2001, Japan’s Ministry of Environment created “100 Scents of Japan”, a curated archive of culturally significant smellscapes tied to regional identity. The catalog revealed how smell functions as a vessel of ecological collective memory. Two decades later however, the physical landscapes that produced these scents have changed. Industrialization, land-use transformation, and air pollution have altered Japan’s atmospheric composition, and with it, its olfactory ecology.
Aeroforms surfaces at the intersection of environmental science and design engineering to address this gap by asking: how can we computationally model scent at a national scale, and what happens when we compare modeled atmospheric composition to cultural memory?
We developed a computational framework for olfactory modeling, a way to approximate the composition of scents across Japan using quantitative environmental data. Through this framework, we constructed a sensory digital twin of Japan’s olfactory environment, integrating agricultural and industrial datasets to estimate the relative presence of ten scent taxonomies across all prefectures. We derived emission factors from literature-based odor threshold values using inverse-potency summation, median log compression, and adaptive normalization. These were then multiplied by prefectural land area to estimate natural emissions, with industrial pollutant emissions incorporated as a parallel weighted contribution layer. The result is a combined, normalized composition vector per prefecture.
By comparing these modeled scent compositions with the “100 Scents of Japan” catalog, we measure where cultural memory aligns with — and diverges from — ecological and industrial reality. This research therefore operates at the intersection of environmental data science, cultural geography, and sensory humanities, developing a reproducible model for environmental perception and introducing a methodology for quantifying perceptual-environmental divergence or, the measurable difference between how people remember environments and how those environments now exist materially.
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